The present invention relates to the field of telecommunications, and in particular, the field of wireless telecommunications. More specifically, the present invention relates to the decoding of telecommunication signals that, prior to transmission, were encoded using turbo coding or similar concatenated coding techniques.
Turbo codes are employed for the purpose of error control when transmitting telecommunications data. In general, turbo coding involves applying two or more component codes to different interleaved versions of the same information sequence prior to transmission. Consequently, decoding involves the use of separate decoders, wherein each of the separate decoders corresponds with one of the aforementioned two or more component codes. Since its introduction in 1993, turbo coding has become well-known in the art. A more detailed discussion of turbo coding can be found, for example, in Berrou et al., xe2x80x9cNear Shannon Limit Errorxe2x80x94Correcting Coding and Decoding: Turbo-Codesxe2x80x9d, IEEE International Communication conference, pp. 1064-1070, May 1993; and in Sklar, xe2x80x9cA Primer on Turbo Code Conceptsxe2x80x9d, IEEE Communications Magazine, pp. 94-102, December 1997.
As one skilled in the art will readily appreciate, decoding an information sequence that has been encoded in accordance with a turbo coding scheme is an iterative process. That is, each of the separate decoders is employed to estimate the a posteriori probability of the information bits associated with the information sequence, wherein extrinsic estimates (derived from the a posteriori information) produced by one decoder become part of the input information for the next decoder. The next decoder then updates the extrinsic estimates. One skilled in the art will also appreciate the fact that in order to achieve good channel (i.e. link) quality and, hence, good system performance, complex, iterative decoding techniques and decoder architectures are required. Such decoding techniques are embodied in, for example, the maximum a posteriori (MAP) algorithm, described more thoroughly in Bahl et al., xe2x80x9cOptimal Decoding of linear Codes for Minimizing Symbol Error Ratexe2x80x9d, IEEE Transactions on Information Theory, vol. 20, pp. 284-287, March 1974; the Log-MAP algorithm, as described in Robertson et al., xe2x80x9cA Comparison of Optimal and Sub-Optimal MAP Decoding Algorithms Operating in the Log Domainxe2x80x9d, IEEE International Communication conference, pp. 1009-1013, 1995; and the modified soft-input Viterbi algorithm (SOVA), as described in Papke et al., xe2x80x9cImproved Decoding with the SOVA in a Parallel Concatenated (Turbo-code) Schemexe2x80x9d, IEEE International Communication conference, pp. 102-106, 1996.
Optimal turbo decoding algorithms, such as those identified above, require knowledge of the channel signal-to-noise ratio (SNR), as stated in Summers et al., xe2x80x9cSNR Mismatch and Online Estimation in Turbo Decodingxe2x80x9d, IEEE Transactions on Communications, vol. 46, no. 4, pp. 421-423, April 1998. These algorithms use SNR, or more specifically, estimated SNR, to produce accurate MAP estimates (i.e., a posteriori estimates) and to blend the a posteriori information associated with the separate decoders. Decoders such as soft-input, soft-output Viterbi decoders, which do not rely upon SNR, are considered suboptimal for turbo decoding purposes.
There are, of course, any number of conventional techniques for deriving an estimate of SNR. The drawbacks associated with such conventional techniques, particularly those employed to support turbo decoding, are the general lack of accuracy in and the variation of the SNR estimates. Although it is important to note that the optimal turbo decoding algorithms identified above tend to be more tolerant of channel SNR overestimations rather than channel SNR underestimations, the general lack of accuracy associated with conventional SNR estimation methods degrades link quality and, hence, system performance.
In order to more favorably exploit the capabilities that turbo coding offers, it would, therefore, be highly desirable to provide more accurate SNR estimations that exhibit little, if any, fluctuation in value to support the process of decoding information sequences that have been encoded using a turbo coding scheme.
The present invention involves the use of more accurate, constant SNR value to support the process of decoding information sequences that have been encoded using turbo coding techniques, particularly in UMTS WCDMA receivers. In general, the present invention provides more accurate, constant SNR value by exploiting the reference SNR that is generated by the power control loop in the receiver for the purpose of generating power control commands. More specifically, the present invention modifies the reference SNR, which is otherwise associated with the power control loop, based on various factors including, but not limited to, the scaling factor that is applied to the decoder input metrics, power settings, processing gains and coding rates.
Accordingly, it is an object of the present invention to provide a more accurate SNR value for turbo decoding purposes, in order to enhance link performance.
It is another object of the present invention to provide a constant, or quasi-constant SNR value for turbo decoding purposes, in order to enhance link performance.
It is yet another object of the present invention to provide a decoder implementation that is less complex.
In accordance with one aspect of the present invention, the above-identified and other objectives are achieved by a receiver, in a telecommunications system, that includes a demodulation unit capable of demodulating a received telecommunications signal and capable of producing scaled, decode input metrics. The receiver also includes a signal-to-noise ratio adaptation unit that is capable of modifying a constant or quasi-constant signal-to-noise value. In addition, the receiver includes a turbo decoder connected to the demodulation unit and the signal-to-noise adaptation unit, wherein said turbo decoder decodes the received signal as a function of the decode input metrics and the modified reference signal-to-noise value.
In accordance with another aspect of the present invention, the above-identified and other objectives are achieved by a wideband code division multiple access (WCDMA) receiver, designed to operate in accordance with a Universal Mobile Telecommunications System (UMTS). the WCDMA receiver includes a receive antenna for receiving a transmitted signal and a demodulation unit connected to the receive antenna, where the demodulation unit is capable of demodulating the received signal from the receive antenna and capable of generating therefrom scaled, decode input metrics. The WCDMA receiver also includes a power control loop connected to the receive antenna, where the power control loop is capable of generating transmit power control commands as a function of a reference signal-to-noise ratio and signal-to-noise ratio estimates which are generated as a function of the received signal. In addition, the receiver employs a signal-to-noise ratio adaptation unit, which is connected to the power control loop, where the signal-to-noise ratio adaptation unit receives and modifies the reference signal-to-noise ratio value. A turbo decoder connected to the demodulation unit and the signal-to-noise ratio adaptation unit then decodes the received signal as a function of the decode input metrics and the modified reference signal-to-noise ratio value.
In accordance with yet another aspect of the present invention, the above-identified and other objectives are achieved by a method for decoding a received signal that was encoded in accordance with a turbo coding scheme. the method involves demodulating the received signal and generating therefrom decode input metrics. The method further involves generating a reference signal-to-noise ratio value as a function of a desired link performance operating point, where the reference signal-to-noise ratio only changes if a different link performance operating point is desired. In addition, the method involves decoding the received signal as a function of the decode input metrics and the reference signal-to-noise ratio.